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Purpose <p>Accurate assessment of residual disease after neoadjuvant chemotherapy (NAC) is essential for surgical planning and prevention of incomplete excision. This study evaluated artificial intelligence (AI)–assisted digital breast tomosynthesis (DBT) as a decision-support tool for post-NAC assessment in breast cancer.</p> Methods <p>This retrospective study included 151 patients treated with NAC followed by surgery. Post-NAC DBT images were analyzed using Lunit INSIGHT DBT. An AI score threshold of 70 was used to classify patients as having low (&lt; 70) or high (≥ 70) scores. In a paired subgroup of 70 patients, pre- and post-NAC AI scores were compared. Diagnostic performance and univariable and multivariable analyses were performed.</p> Results <p>The overall pathological complete response (pCR) rate was 41.1% (62/151). Patients with low AI scores had higher pCR rates than those with high scores (49.5% vs. 28.3%, <i>p</i> = 0.010). High AI scores were associated with persistent microcalcifications and residual ductal carcinoma in situ (<i>p</i> &lt; 0.001). Overall accuracy was 58.3% and varied by molecular subtype, with higher performance in triple-negative breast cancer and limited discrimination in HR+/HER2 − tumors. In multivariable analysis, AI score showed borderline significance (<i>p</i> = 0.070). In the paired cohort, AI scores decreased after NAC (<i>p</i> &lt; 0.001), but delta change did not differ between pCR and non-pCR groups (<i>p</i> = 0.877).</p> Conclusion <p>AI-assisted DBT may have potential complementary value as a decision-support tool for post-NAC surgical planning, particularly in triple-negative breast cancer and in cases with residual microcalcifications.</p>

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AI-assisted digital breast tomosynthesis as a decision-support tool for post-neoadjuvant surgical planning in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Adnan Gündoğdu,
  • Nur Betül Ünal Özdemir,
  • Ceyda Sönmez Wetherilt,
  • Merve Aktaş,
  • Damiano Gentile,
  • Bülent Çomçalı,
  • Osman Cem Yılmaz

摘要

Purpose

Accurate assessment of residual disease after neoadjuvant chemotherapy (NAC) is essential for surgical planning and prevention of incomplete excision. This study evaluated artificial intelligence (AI)–assisted digital breast tomosynthesis (DBT) as a decision-support tool for post-NAC assessment in breast cancer.

Methods

This retrospective study included 151 patients treated with NAC followed by surgery. Post-NAC DBT images were analyzed using Lunit INSIGHT DBT. An AI score threshold of 70 was used to classify patients as having low (< 70) or high (≥ 70) scores. In a paired subgroup of 70 patients, pre- and post-NAC AI scores were compared. Diagnostic performance and univariable and multivariable analyses were performed.

Results

The overall pathological complete response (pCR) rate was 41.1% (62/151). Patients with low AI scores had higher pCR rates than those with high scores (49.5% vs. 28.3%, p = 0.010). High AI scores were associated with persistent microcalcifications and residual ductal carcinoma in situ (p < 0.001). Overall accuracy was 58.3% and varied by molecular subtype, with higher performance in triple-negative breast cancer and limited discrimination in HR+/HER2 − tumors. In multivariable analysis, AI score showed borderline significance (p = 0.070). In the paired cohort, AI scores decreased after NAC (p < 0.001), but delta change did not differ between pCR and non-pCR groups (p = 0.877).

Conclusion

AI-assisted DBT may have potential complementary value as a decision-support tool for post-NAC surgical planning, particularly in triple-negative breast cancer and in cases with residual microcalcifications.